1. Field of the Invention
The present invention relates to a single-pegging-based product supply chain management system, which manages a supply chain of products with respect to the demand for end products in a continuous process (hereinafter referred to as a staged production process) of supplying products to produce other products.
Moreover, the present invention relates to a single-pegging-based product supply chain management system, which represents a supply chain of each product as a node and converts a multi-pegging process (graph) comprising a plurality of nodes in each stage into single-pegging graphs each comprising a single node in each stage by division.
2. Discussion of Related Art
In general, the purpose of supply chain management is to establish and execute optimal production planning to meet the demand for end products (such as a product, quantity, delivery date, etc.), thus achieving inventory reduction and customer satisfaction.
For the supply chain management, it is necessary to connect the flows of materials, products, and costs, which occur in the overall business activities such as purchase, supply, production, sales, etc., to each other thus identifying the flows as a single flow of information. Moreover, it is necessary to identify possible problems and improvement points by analyzing the flow of information. According to circumstances, it is necessary to allow a system to automatically cope with the possible problems.
In particular, when a manufacturer proceeds with production or establishes production planning, the manufacturer is subject to restrictions on the process capacity of equipment, the procurement capacity from part suppliers, etc. and may establish the production planning based on these restrictions. That is, the production planning is established based on the restrictions on the process capacity of production equipment, etc. Moreover, there are resources used to produce certain semi-finished products or finished products in the production process. As such, the resources related to the production capacity refers to supply capacity, which serves as a restriction on the modeling of the amount of resources used and the establishment of production planning when the manufacturer is to produce products.
When the above-described supply chain management system is established, it is possible to immediately establish production planning by predicting the demand for end products by consumers and to place orders with suppliers for parts by predicting the supply of resources such as parts. That is, it is possible to supply products that consumers want on desired dates by immediate production based on the timely supply of parts and the process capacity of production equipment. Thus, it is possible to increase consumer satisfaction, reduce costs, and ensure competitiveness by minimizing inventory.
Here, it is necessary to establish production planning for each production line based on the estimated demand by reflecting various restriction conditions on the production. However, it is very complex and difficult to establish production planning based on a variety of restriction conditions such as the number of production lines, the number of resources used, the number of part suppliers, etc. which are to be considered in a typical manufacturing site.
To this end, the production planning is established based on a linear programming algorithm (LPA) in which each restriction condition is represented by a formula. That is, a solution is found by determining the restriction condition on the production and the target amount (or demand) of products based on the linear programming algorithm. Accordingly, in order to produce the target amount of end products, the amount of products to be produced on each production line or the amount of resources such as parts required for the production is calculated. Based on this calculation result, a supply chain manager establishes production planning in a particular supply chain.
As shown in FIG. 1, the result may be represented in the form of a hierarchical directed graph (or multi-pegging graph) by the linear programming algorithm. In FIG. 1, A represents a group of end products, B represents products (or intermediate products) for producing the end products of A, and C represents products (or supply products) to be supplied to produce the products of B. The result has an output (or input) at each node as a solution (the output is not shown in FIG. 1), and thus the production planning can be established based on the output.
That is, each node of FIG. 1 refers to the product (or part), and each link connects a production process or a product (or part) to be supplied to produce a corresponding product (or part). Accordingly, A refers to the final demand, and it is possible to identify subordinate products for producing the end products of A by reversely following the flow (link) from A. Moreover, it is possible to identify the amount of products (or parts) required for the final demand A in each stage.
However, in the graph (or multi-pegging graph) according to the prior art as shown in FIG. 1, the connection of the links is complex, and thus it is not easy to cross check the links on the upstream or downstream. In particular, when there are hundreds and thousands of parts or scores of manufacturing processes, the multi-pegging graph becomes very complex.
In other words, according to the conventional method of establishing production planning, a solution is found by applying the linear programming algorithm to the multiple stages of semi-finished products (or intermediate products) to produce finished products (or end products). However, in the event of a failure, it is very difficult to determine at which stage the failure occurs, since the production planning solution is in the form of a multi-pegging graph.
Accordingly, it is difficult for the supply chain manager to analyze the multi-pegging graph as the result by the linear programming algorithm, and it takes a lot of time to establish production planning based on the multi-pegging graph.